Due to the large number of vulnerabilities in information systems and the continuous activity of attackers, techniques for malicious traffic detection are required to identify and protect against cyber-attacks. Therefore, it is important to intentionally operate a cyber environment to be invaded and compromised in order to allow security professionals to analyze the evolution of the various attacks and exploited vulnerabilities.This dataset includes 2016, 2017 and 2018 cyber attacks in the HoneySELK environment.HoneySELK was developed to control, capture, analyze and visualize new and unknown attacks in real time within the research laboratory of the Electrical Engineering Department of the University of Brasília. - Rodrigues, G.A.P.; Albuquerque, R.d.O.; de Deus, F.E.G.; de Sousa, R.T., Jr.; de Oliveira Júnior, G.A. Cybersecurity and Network Forensics: Analysis of Malicious Traffic towards a Honeynet with Deep Packet Inspection. Appl. Sci. 2017, 7, 1082 (https://www.mdpi.com/2076-3417/7/10/1082).- Oliveira Júnior, G.A.; de Sousa, R.T., Jr.; de Albuquerque, R.O.; Canedo, E.D.; Grégio, A. HoneySELK: Um Ambiente para Pesquisa e Visualização de Ataques Cibernéticos em Tempo Real. In Proceedings of the XVI Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais, Niteroi, Rio de Janeiro, Brazil, 7–10 November 2016; pp. 697–706 (http://sbseg2016.ic.uff.br/pt/anais.php and https://repositorio.unb.br/handle/10482/22886).- Oliveira Jr, G. A., Sousa Jr, R. T. de, Tenório, D. F. (2015). Desenvolvimento de um Ambiente Honeynet Virtual para Aplicação Governamental. In: The Ninth International Conference on Forensic Computer Science. v. 1. p. 70-80 (http://www.icofcs.org/2015/papers-published-009.html).

Did the COVID-19 pandemic really affect cybersecurity? Short answer – Yes. Cybercrime is up 600% due to COVID-19.

This dataset was generated on a small-scale process automation scenario using MODBUS/TCP equipment, for research on the application of ML techniques to cybersecurity in Industrial Control Systems. The testbed emulates a CPS process controlled by a SCADA system using the MODBUS/TCP protocol. It consists of a liquid pump simulated by an electric motor controlled by a variable frequency drive (allowing for multiple rotor speeds), which in its turn controlled by a Programmable Logic Controller (PLC). The motor speed is determined by a set of predefined liquid temperature thresholds, whose measurement is provided by a MODBUS Remote Terminal Unit (RTU) device providing a temperature gauge, which is simulated by a potentiometer connected to an Arduino. The PLC communicates horizontally with the RTU, providing insightful knowledge of how this type of communications may have an effect on the overall system. The PLC also communicates with the Human-Machine Interface (HMI) controlling the system. The testbed is depicted in the image hereby included.The provided sample corresponds to roughly one third of the total available captured traces.The full network trace data sets are available at: https://github.com/tjcruz-dei/ICS_PCAPS/releases/tag/MODBUSTCP%231 This dataset was produced as part of the research effort for the ATENA H2020 EC project (H2020-DS-2015-1 700581). Citation RequestFrazão, I. and Pedro Henriques Abreu and Tiago Cruz and Araújo, H. and Simões, P. , "Denial of Service Attacks: Detecting the frailties of machine learning algorithms in the Classication Process", in 13th International Conference on Critical Information Infrastructures Security (CRITIS 2018), ed. Springer, Kaunas, Lithuania, September 24-26, 2018, Springer series on Security and Cryptology , 2018. DOI: 10.1007/978-3-030-05849-4_19

The financial stability of an organization depends in part upon managing risk and vulnerabilities. In today’s technology-dependent business environment, a strategy for limiting an organization’s risk and vulnerability must include a solid and well-managed cyber security program. The National Institute of Standards and Technology (NIST) provides a Cyber Security Framework (CSF) for benchmarking and measuring the maturity level of cyber security programs across all industries. The City uses this framework and toolset to measure and report on its internal cyber security program. The City’s goal is to progressively achieve and maintain a 90%, or better, maturity rating across all 22 critical infrastructure categories within the standard.This page provides information for the Cyber Security performance measure.DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this page, please visit https://tempegov.hub.arcgis.com:/overview/edit.

o Top 10 For Everyone o Put Your Money Where Your Data Is – Invest In Cyber Security o CSE’s Assessment On Cyber Threats To Canada’s Democratic Process o The Forecast On Cloud Computing o Cyber Hygiene Series: Social Media o CSE In The Community

The National Institute of Standards and Technology (NIST) provides a Cybersecurity Framework (CSF) for benchmarking and measuring the maturity level of cyber security programs across all industries. The City uses this framework and toolset to measure and report on its internal cyber security program.The foundation for this measure is the Framework Core, a set of cybersecurity activities, desired outcomes and applicable references that are common across critical infrastructure/industry sectors. These activities come from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) published standard, along with the information security and customer privacy controls it references (NIST 800 Series Special Publications). The Framework Core presents industry standards, guidelines, and practices in a manner that allows for communication of cybersecurity activities and outcomes across the organization from the executive level to the implementation/operations level. The Framework Core consists of five concurrent and continuous functions – identify, protect, detect, respond, and recover. When considered together, these functions provide a high-level, strategic view of the lifecycle of an organization’s management of cybersecurity risk. The Framework Core identifies underlying key categories and subcategories for each function, and matches them with example references, such as existing standards, guidelines and practices for each subcategory. This page provides data for the Cybersecurity performance measure.Cybersecurity Framework (CSF) scores by each CSF category per fiscal year quarter (Performance Measure 5.12)The performance measure dashboard is available at 5.12 Cybersecurity.Additional InformationSource: Maturity assessment /https://www.nist.gov/topics/cybersecurityContact: Scott CampbellContact E-Mail: Scott_Campbell@tempe.govData Source Type: ExcelPreparation Method: The data is a summary of a detailed and confidential analysis of the city's cyber security program. Maturity scores of subcategories within NIST CFS are combined, averaged and rolled up to a summary score for each major category.Publish Frequency: AnnualPublish Method: ManualData Dictionary

We inspect 965 cybersecurity research papers published between 2012 and 2016 in order to understand better how datasets are used, produced and shared. We construct a taxonomy of the types of data created and shared, informed and validated by the examined papers. We then analyze the gathered data on datasets. Three quarters of existing datasets used as input to research are publicly available, but less than 20% of datasets created by researchers are publicly shared. Using a series of linear regressions, we demonstrate that those researchers who do make public the datasets they create are rewarded with more citations to the associated papers. Hence, we conclude that an under-appreciated incentive exists for researchers to share their created datasets with the broader research community.

The National Institute of Standards and Technology (NIST) provides a Cybersecurity Framework (CSF) for benchmarking and measuring the maturity level of cyber security programs across all industries. The City uses this framework and toolset to measure and report on its internal cyber security program.The foundation for this measure is the Framework Core, a set of cybersecurity activities, desired outcomes and applicable references that are common across critical infrastructure/industry sectors. These activities come from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) published standard, along with the information security and customer privacy controls it references (NIST 800 Series Special Publications). The Framework Core presents industry standards, guidelines, and practices in a manner that allows for communication of cybersecurity activities and outcomes across the organization from the executive level to the implementation/operations level. The Framework Core consists of five concurrent and continuous functions – identify, protect, detect, respond, and recover. When considered together, these functions provide a high-level, strategic view of the lifecycle of an organization’s management of cybersecurity risk. The Framework Core identifies underlying key categories and subcategories for each function, and matches them with example references, such as existing standards, guidelines and practices for each subcategory. This page provides data for the Cybersecurity performance measure.Cybersecurity Framework cumulative score summary per fiscal year quarter (Performance Measure 5.12)The performance measure page is available at 5.12 Cybersecurity.Additional InformationSource: Maturity assessment / https://www.nist.gov/topics/cybersecurityContact: Scott CampbellContact E-Mail: Scott_Campbell@tempe.govData Source Type: ExcelPreparation Method: The data is a summary of a detailed and confidential analysis of the city's cyber security program. Maturity scores of subcategories within NIST CFS are combined, averaged and rolled up to a summary score for each major category.Publish Frequency: AnnualPublish Method: ManualData Dictionary

In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer. In each layer, we propose new emerging technologies that satisfy the key requirements of IoT and IIoT applications, such as, ThingsBoard IoT platform, OPNFV platform, Hyperledger Sawtooth, Digital twin, ONOS SDN controller, Mosquitto MQTT brokers, Modbus TCP/IP, ...etc. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, ...etc.). However, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes.

The cybersecurity NER corpus 2019 contains two corpora: soft_flaw - 1000 binary annotated tweets (TRUE: tweet mentions a software/system/device related security issue (vulnerability, exploit, patch), a malware, or a hacking method; FALSE: otherwise) class distribution: TRUE - 283, FALSE - 717 soft_flaw_NER - ca. 1000 NER annotations marking the name of the software/system/device/company with a security related issue, or the name of a malware The same tweet might be included in both corpora, however the vast majority of tweets is different across two corpora. Files are in the jsonl format.

The files contain our firm-level measure of cybersecurity risk as well as replication codes in SAS & STATA for our study entitled "Cybersecurity Risk"

To determine the effectiveness of any defense mechanism, there is a need for comprehensive real-time network data that solely references various attack scenarios based on older software versions or unprotected ports, and so on. This presented dataset has entire network data at the time of several cyber attacks to enable experimentation on challenges based on implementing defense mechanisms on a larger scale. For collecting the data, we captured the network traffic of configured virtual machines using Wireshark and tcpdump. To analyze the impact of several cyber attack scenarios, this dataset presents a set of ten computers connected to Router1 on VLAN1 in a Docker Bridge network, that try and exploit each other. It includes browsing the web and downloading foreign packages including malicious ones. Also, services like FTP and SSH were exploited using several attack mechanisms. The presented dataset shows the importance of updating and patching systems to protect themselves to a greater extent, by following attack tactics on older versions of packages as compared to the newer and updated ones. This dataset also includes an Apache Server hosted on the different subset on VLAN2 which is connected to the VLAN1 to demonstrate isolation and cross-VLAN communication. The services on this web server were also exploited by the previously stated ten computers. The attack types include: Distributed Denial of Service, SQL Injection, Account Takeover, Service Exploitation (SSH, FTP), DNS and ARP Spoofing, Scanning and Firewall Searching and Indexing (using Nmap), Hammering the services to brute-force passwords and usernames, Malware attack, Spoofing and Man-in-the-Middle Attack. The attack scenarios also show various scanning mechanisms and the impact of Insider Threats on the entire network.

India Cyber Security Incidents: Total data was reported at 1,391,457.000 Unit in 2022. This records a decrease from the previous number of 1,402,809.000 Unit for 2021. India Cyber Security Incidents: Total data is updated yearly, averaging 49,455.000 Unit from Dec 2004 to 2022, with 19 observations. The data reached an all-time high of 1,402,809.000 Unit in 2021 and a record low of 23.000 Unit in 2004. India Cyber Security Incidents: Total data remains active status in CEIC and is reported by Indian Computer Emergency Response Team. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TF010: Information Technology Statistics: Cyber Security Incidents.

BackgroundHealthcare is facing a growing threat of cyberattacks. Myriad data sources illustrate the same trends that healthcare is one of the industries with the highest risk of cyber infiltration and is seeing a surge in security incidents within just a few years. The circumstances thus begged the question: are US hospitals prepared for the risks that accompany clinical medicine in cyberspace?ObjectiveThe study aimed to identify the major topics and concerns present in today's hospital cybersecurity field, intended for non-cyber professionals working in hospital settings.MethodsVia structured literature searches of the National Institutes of Health's PubMed and Tel Aviv University's DaTa databases, 35 journal articles were identified to form the core of the study. Databases were chosen for accessibility and academic rigor. Eighty-seven additional sources were examined to supplement the findings.ResultsThe review revealed a basic landscape of hospital cybersecurity, including primary reasons hospitals are frequent targets, top attack methods, and consequences hospitals face following attacks. Cyber technologies common in healthcare and their risks were examined, including medical devices, telemedicine software, and electronic data. By infiltrating any of these components of clinical care, attackers can access mounds of information and manipulate, steal, ransom, or otherwise compromise the records, or can use the access to catapult themselves to deeper parts of a hospital's network. Issues that can increase healthcare cyber risks, like interoperability and constant accessibility, were also identified. Finally, strategies that hospitals tend to employ to combat these risks, including technical, financial, and regulatory, were explored and found to be weak. There exist serious vulnerabilities within hospitals' technologies that many hospitals presently fail to address. The COVID-19 pandemic was used to further illustrate this issue.ConclusionsComparison of the risks, strategies, and gaps revealed that many US hospitals are unprepared for cyberattacks. Efforts are largely misdirected, with external—often governmental—efforts negligible. Policy changes, e.g., training employees in cyber protocols, adding advanced technical protections, and collaborating with several experts, are necessary. Overall, hospitals must recognize that, in cyber incidents, the real victims are the patients. They are at risk physically and digitally when medical devices or treatments are compromised.

Cyber attacks are a growing concern for small businesses during COVID-19 . Be Protected While You Work. Upgrade Your Small Business's Virus Protection Today! Before going for a Cyber security solutions for small to mid-sized businesses deliver enterprise-level protection.

Download this (Checklist for a Small Firm's Cybersecurity Program 2020-2021) data set to deploy secure functioning of various aspects of your small business including, employee data, website and more.This checklist is provided to
assist small member firms with limited resources to establish a cybersecurity program to identify and assess cybersecurity threats,
protect assets from cyber intrusions,
detect when their systems and assets have been compromised,
plan for the response when a compromise occurs and implement a plan to recover lost, stolen or unavailable assets. 
Train employees in security principles.
Protect information, computers, and networks from malware attacks.
Provide firewall security for your Internet connection.
Create a mobile device action plan.
 Make backup copies of important business data and information.
 Learn about the threats and how to protect your website.
 Protect Your Small Business site.
 Learn the basics for protecting your business web sites from cyber attacks at WP Hacked Help Blog

Created With Inputs From Security Experts at WP Hacked Help - Pioneer In WordPress Malware Removal & Security

This sheet contains the answers from our european Cyber Security MSc Education Survey. The data shows which knowledge units various educations in Europe cover and to which extend. We drew conclusions in the paper "Are We Preparing Students to Build Security In? A Survey of European Cybersecurity in Higher Education Programs". The present dataset is newer and therefore extends the one we based our paper on.

The CSIAC-DoDIN (V1.0) dataset collects cybersecurity-related policies and issuances developed by the DoD Deputy CIO for Cybersecurity. The dataset is based on a knowledge base that clusters and classifies these policies and provides an organizational structure. The dataset includes annotated documents with policies, responsibilities, procedures, classification, purpose, scope, and applicability. The dataset also includes cluster and subcluster classification, type classification, and text entailment. The dataset is available for research and experimentation, and baseline performances using transformer language models have been provided. The limitations of the dataset include its focus on DoD cybersecurity policies, the English language, and the provided tasks. The dataset can serve as a benchmark and basis for future cybersecurity policy datasets and applications. Still, caution should be exercised regarding potential risks and biases associated with transformer language models.

These cybersecurity statistics will help you understand the state of online security and give you a better idea of what it takes to protect yourself.

Explore the importance of cyber security in medical devices and the measures needed to protect patient data and ensure device integrity.